Data Lineage

Data collected directly by an organisation from its own customers, users, or audience through owned channels such as websites, apps, CRM systems, transactions, and surveys. First-party data is considered the most valuable data category because it is collected with consent, is unique to the organisation, and provides direct insight into customer behaviour and preferences.

The principle that data is subject to the laws and governance structures of the country in which it is collected or stored. Data sovereignty requirements affect cloud computing architecture, cross-border data transfers, and vendor selection, particularly in light of GDPR restrictions on transfers to countries without adequate data protection standards.

Artificially generated data that mimics the statistical properties of real-world datasets, used to train machine learning models when actual data is scarce, sensitive, or expensive to obtain. Synthetic data enables AI development in privacy-constrained domains such as healthcare and finance, while reducing data acquisition costs and regulatory exposure.

The branch of applied ethics concerned with the moral implications of designing, deploying, and using artificial intelligence systems. AI ethics addresses issues including fairness, transparency, privacy, accountability, and the societal impact of automation.

Systematic and repeatable errors in an AI system's outputs that create unfair outcomes for particular groups, typically arising from biased training data, flawed model design, or unrepresentative sampling. Algorithmic bias poses significant reputational, legal, and regulatory risks, and its identification and mitigation are core components of responsible AI governance.

A chronological record of system activities, transactions, or document changes that provides a verifiable history of who did what, when, and why. Audit trails are essential for regulatory compliance, fraud detection, and internal controls, and are required by standards including SOX, GDPR, and ISO 27001.

A centralised platform for storing, managing, and serving the engineered features (input variables) used by machine learning models in both training and real-time inference. Feature stores ensure consistency between training and production environments, enable feature reuse across multiple ML models, reduce duplication of feature engineering effort, and provide a governance layer for tracking feature lineage and ownership.

A framework for developing and deploying artificial intelligence systems that are fair, transparent, accountable, and aligned with human values and societal well-being. Responsible AI encompasses technical practices such as bias testing and model interpretability, alongside governance processes including ethical review boards, impact assessments, and stakeholder engagement.

Data as a Balance Sheet Asset: Valuation Methods for the AI Era

Data is now formally recognised as productive capital by the System of National Accounts. But how do you value it on a balance sheet? Tony Hillier draws on 30 years of structured finance experience to map three valuation approaches — and where data assets fit into modern capital structures.